Segmentation-based modeling for advanced targeted marketing
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Approximately Optimal Approximate Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Policy mining: learning decision policies from fixed sets of data
Policy mining: learning decision policies from fixed sets of data
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Privacy-preserving reinforcement learning
Proceedings of the 25th international conference on Machine learning
Use of Reinforcement Learning in Two Real Applications
Recent Advances in Reinforcement Learning
Assigning discounts in a marketing campaign by using reinforcement learning and neural networks
Expert Systems with Applications: An International Journal
A new marketing strategy map for direct marketing
Knowledge-Based Systems
Optimizing debt collections using constrained reinforcement learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Noisy reinforcements in reinforcement learning: some case studies based on gridworlds
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
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The issues of cross channel integration and customer life time value modeling are two of the most important topics surrounding customer relationship management (CRM) today. In the present paper, we describe and evaluate a novel solution that treats these two important issues in a unified framework of Markov Decision Processes (MDP). In particular, we report on the results of a joint project between IBM Research and Saks Fifth Avenue to investigate the applicability of this technology to real world problems. The business problem we use as a testbed for our evaluation is that of optimizing direct mail campaign mailings for maximization of profits in the store channel. We identify a problem common to cross-channel CRM, which we call the Cross-Channel Challenge, due to the lack of explicit linking between the marketing actions taken in one channel and the customer responses obtained in another. We provide a solution for this problem based on old and new techniques in reinforcement learning. Our in-laboratory experimental evaluation using actual customer interaction data show that as much as 7 to 8 per cent increase in the store profits can be expected, by employing a mailing policy automatically generated by our methodology. These results confirm that our approach is valid in dealing with the cross channel CRM scenarios in the real world.